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We hope we got the facts right herehttps://machinelearningknowledge.ai/brief-history-of-deep-learning/ …
I am disappointed that @drfeifei is not credited in this or numerous other articles on history of #DeepLearning (not to be confused with history of #AI). She should have been one of the #turing award winners. Because without #imagenet none of this would have happened.
I agree! It's disappointing that she's not included as one of the pioneers when Deep Learning in vision stands on the giant shoulders of Imagenet.
The problem of not valuing data (training) runs deeper than just Imagenet case. It's the case with almost any data scientist. Most of the data science enthusiasts are quite excited about the high accuracy their algorithm can reap but fail to see value of Imagenet or mnist
Alas, Rosalind Franklin, and others, indeed.
Title should be "Male only history of deep learning". @drfeifei 's work always pushes boundaries of deep learning and computer vision. Object recognition (imagenet), grounding (visual genome), reasoning (clevr) and many more!
And this is happening to those who have spent their careers in elite US institutions -- imagine those without this privilege.
This article is so ignorant and annoying. The picture is shouting that there is something very wrong with it. I almost don't want to say anything
and then we complain about not enough women doing STEM.
I owe her Ted talk which inspired me to pursue Computer Vision as a research direction, then as a career path
I just watched a nice intro to DL from @JonKrohnLearns (on O'Reilly), and @drfeifei and #imagenet get a prominent mention. Best summary perspective on the history of AI, ML, and DL I've seen.
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